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HighlightsSubsurface drip irrigation (SDI) has continued to expand in irrigation area within the U.S. during the last 15 years.Research with SDI continues for multiple crop types (fiber, grain and oilseed, horticultural, forage, and turf).SDI usage on many crops has matured through research and development of appropriate strategies and technologiesDespite some persistent challenges to successful use of SDI, important opportunities exist for further adoption.Abstract. Subsurface drip irrigation (SDI) offers several advantages over alternative irrigation systems when it is designed and installed correctly and when best management practices are adopted. These advantages include the ability to apply water and nutrients directly and efficiently within the crop root zone. Disadvantages of SDI in commercial agriculture relative to alternative irrigation systems include greater capital cost per unit land area (except for small land parcels), unfamiliar management and maintenance protocols that can exacerbate the potential for emitter clogging, the visibility of system attributes (components and design characteristics) and performance, and the susceptibility to damage (i.e., rodents and tillage) of the subsurface driplines. Despite these disadvantages, SDI continues to be adopted in commercial agriculture in the U.S., and research efforts to evaluate and develop SDI systems continue as well. This article summarizes recent progress in research (2010 to 2020) and the status of commercial adoption of SDI, along with a discussion of current challenges and future opportunities. Keywords: Drip Irrigation, Irrigation, Irrigation systems, Microirrigation, SDI, Water management.

Crop nitrogen (N) status is known to affect crop water status and crop water use. To investigate further the N effects on soil water changes and on canopy temperature, three water levels × four N levels were imposed on two growing seasons of maize in west central Nebraska, USA. Soil water changes were measured using a neutron probe, whereas canopy temperature was measured using infrared thermometers on a ground-based mobile platform. At all water levels, soil water losses over month-long intervals were generally greater as N levels increased. Given equal water levels, early afternoon canopy temperatures were usually lower with higher N levels, but no trend or even the opposite trend was occasionally observed. Jointly considering canopy reflectance and soil water depletion shows potential to explain much of the variation in estimated instantaneous water use among plots. However, determining the relative contributions of the canopy and soil factors on a particular day may require season-to-date knowledge of the crop. Further research on assimilating such sensor data for a combined stress coefficient would improve crop modeling and irrigation scheduling when variable water sufficiency and variable N sufficiency are simultaneously significant.

Highlights Within-field variability was larger for individual depths than for the profile average across multiple depths. Distributions of the profile average were approximately normal, with increasing variances as the soil was drying. Probability theory was applied to quantify the effect of sensor set number on irrigation scheduling. The benefit of additional sensors sets may decrease for longer irrigation cycles and for more heterogeneous fields. Abstract. Even when located within the same field, multiple units of the same soil moisture sensor rarely report identical values. Such within-field variability in soil moisture sensor data is caused by natural and manmade spatial heterogeneity and by inconsistencies in sensor construction and installation. To better describe this variability, daily soil water tension values from 14 to 23 sets of granular matrix sensors during the middle part of four soybean site-years in the Mississippi Delta were analyzed. The soil water tension data were found to follow approximately normal distributions, to exhibit moderately high temporal rank stability, and to show strong positive correlation between mean and variance. Based on these observations and the existing literature, a probabilistic conceptual framework was proposed for interpreting within-field variability in granular matrix sensor data. This framework was then applied to investigate the impact of sensor set number (i.e., number of replicates) and irrigation triggering threshold on the scheduling of single-day and multi-day irrigation cycles. If a producer’s primary goal of irrigation scheduling is to keep soil water adequate in a particular fraction of land on average, the potential benefit from increasing sensor set number may be smaller than traditionally expected. Improvement, expansion, and validation of this probabilistic framework are welcomed for developing a practical and robust approach to selecting the sensor set number and the irrigation triggering threshold for diverse soil moisture sensor types in diverse contexts. Keywords: Irrigation scheduling, Probability, Sensors, Soil moisture, Soil water tension, Variability, Watermark.

HIGHLIGHTSUniversity extension has been playing a larger role, serving a larger number of irrigated farms.Extension programs in irrigation water management (IWM) have been transitioning away from lectures and field tours as the primary means of knowledge transfer.New IWM programs focus on experiential learning, development of practitioner networks, and industry participation.Abstract. Promotion and adoption of irrigation water management (IWM) technology, tools, and best management practices are important as water availability concerns are addressed. Traditional extension programs have relied on lecture presentations, field tours, fact sheets, and on-station demonstrations to promote IWM practices and tools. However, these platforms tend not to provide the experience and opportunity for growers to identify and become comfortable with innovative solutions, such as new technology. To address these challenges and to appeal to an ever-changing client base, innovative and locally relevant extension and outreach programs have been devised to engage and educate growers. This article describes some of these programs that extend beyond previous traditional programs to connect growers with IWM. Keywords: Demonstrations, Experiential learning, Grower competition, Outreach, Practitioner networks.

HighlightsCapacitance-based electromagnetic soil moisture sensors were tested in disturbed and undisturbed soils.The uncertainty in estimation of soil water depth was lower using the undisturbed soil sample calibrations.The uncertainty in estimation of soil water depletion was lower than the uncertainty in volumetric water content.Undisturbed calibration of water depletion quantifies water demand with better precision and avoids over-watering.Abstract. The physical properties of soil, such as structure and texture, can affect the performance of an electromagnetic sensor in measuring soil water content. Historically, calibrations have been performed on repacked samples in the laboratory and on soils in the field, but little research has been done on laboratory calibrations with intact (undisturbed) soil cores. In this study, three replications each of disturbed and undisturbed soil samples were collected from two soil texture classes (Yutan silty clay loam and Fillmore silt loam) at a field site in eastern Nebraska to investigate the effects of soil structure and texture on the precision of a METER Group GS-1 capacitance-based sensor calibration. In addition, GS-1 sensors were installed in the field near the soil collection sites at three depths (0.15, 0.46, and 0.76 m). The soil moisture sensor had higher precision in the undisturbed laboratory setup, as the undisturbed calibration had a better correlation [slope closer to one, R2undisturbed (0.89) > R2disturbed (0.73)] than the disturbed calibrations for the Yutan and Fillmore texture classes, and the root mean square difference using the laboratory calibration (RMSDL) was higher for pooled disturbed samples (0.053 m3 m-3) in comparison to pooled undisturbed samples (0.023 m3 m-3). The uncertainty in determination of volumetric water content (?v) was higher using the factory calibration (RMSDF) in comparison to the laboratory calibration (RMSDL) for the different soil structures and texture classes. In general, the uncertainty in estimation of soil water depth was greater than the uncertainty in estimation of soil water depletion by the sensors installed in the field, and the uncertainties in estimation of depth and depletion were lower using the calibration developed from the undisturbed soil samples. The undisturbed calibration of soil water depletion would determine water demand with better precision and potentially avoid over-watering, offering relief from water shortages. Further investigation of sensor calibration techniques is required to enhance the applicability of soil moisture sensors for efficient irrigation management. Keywords: Calibration, Capacitance, Depletion, Irrigation, Precision, Sensor, Soil water content, Structure, Uncertainty.

Data‐driven models used for predicting soil temperature usually have increasing errors with increasing depth. By exploring the integration of knowledge‐based and machine learning approaches, this study used a novel transformation of meteorological variables to increase prediction accuracy of soil temperature with increasing soil depth. Using datasets for two soil textures (silty clay loam and loamy coarse sand) at two locations with different climates, predictive models were developed for five depths (5, 10, 20, 50, and 100 cm) as a function of meteorological features using an adaptive neuro‐fuzzy inference system (ANFIS). For each depth, soil temperature was predicted with nontransformation (NT), autocorrelation (AC), moving average (MA), and a combination of transformations (NT‐ACMA) of meteorological features. Across all depths, the predictive accuracy of NT‐ACMA models was significantly higher than that of NT, AC, and MA models for both soil textures (R2 = .99, RMSE = 1 °C for silty clay loam; R2 = .99, RMSE = 1.2 °C for loamy coarse sand), with increasing prediction accuracy as soil depth increases. Results for different soil textures and climates in 11 locations across the contiguous United States show that, except for 100‐cm depth, there seems to be significant positive linear relationships between the best moving average and the solar inclination. This makes our NT‐ACMA technique transferable to any location in the contiguous United States irrespective of the location, climate, and soil texture. We conclude that integrating lag times and moving averages of meteorological features can lead to better prediction of soil temperature at different soil depths.

The interpretation of optical canopy sensor readings for determining optimal rates of late-season site-specific nitrogen application to corn (Zea mays L.) can be complicated by spatially variable water sufficiency, which can also affect canopy size and/or pigmentation. In 2017 and 2018, corn following corn and corn following soybeans were subjected to irrigation × nitrogen fertilizer treatments in west central Nebraska, USA, to induce variable water sufficiency and variable nitrogen sufficiency. The vegetation index-sensor combinations investigated were the normalized difference vegetation index (NDVI), the normalized difference red edge index (NDRE), and the reflectance ratio of near infrared minus red edge over near infrared minus red (DATT) using ACS-430 active optical sensors; NDVI using SRS-NDVI passive optical sensors; and red brightness and a proprietary index using commercial aerial visible imagery. Among these combinations, NDRE and DATT were found to be the most suitable for assessing nitrogen sufficiency within irrigation levels. While DATT was the least sensitive to variable water sufficiency, DATT still tended to decrease with decreasing water sufficiency in high nitrogen treatments, whereas the effect of water sufficiency on DATT was inconsistent in low nitrogen treatments. A new method of quantifying nitrogen sufficiency while accounting for water sufficiency was proposed and generally provided more consistent improvement over the mere averaging of water effects as compared with the canopy chlorophyll content index method. Further elucidation and better handling of water-nitrogen interactions and confounding are expected to become increasingly important as the complexity, automation, and adoption of sensor-based irrigation and nitrogen management increase.

The presence of clay particles increases the specific surface area of a soil and can affect the calibration of electromagnetic soil water sensors including reflectometers. To quantify and correct for this effect in two relatively new reflectometers, three TDR315 and three CS655 sensors were installed in each of five soils with clay content ranging from 5 to 49%. As the soils were dried in a temperature controlled room, sensor reported soil volumetric water content (θv) according to the factory calibration was compared against reference θv determined by weighing the soils. Sensor reported θv was similar to reference θv in the sand soil (root mean square difference (RMSD) 0.93) for both sensors. By estimating calibration coefficients based on clay content alone to recalculate sensor θv from sensor reported apparent relative permittivity, RMSD from reference θv was reduced by approximately 36% for both sensors as compared with using the factory calibration. Applying the same procedure to independent literature data tended to improve θv accuracy of both sensors increasingly as clay content increased above 20%. The findings suggest that a simple, user-friendly correction for clay content effects may provide initial practical improvement over the factory calibration of a reflectometer in clayey soils.

Nebraskan streams contribute excess nitrogen to the Mississippi/Atchafalaya River Basin and Gulf of Mexico, which results in major water-quality impairments. Reducing the amount of nitrogen (N) exported in these streams requires the use of best management practices (BMPs) within the landscape. However, proper BMP utilization has rarely been statistically connected to potential controls of N export within watersheds, particularly precipitation and soil characteristics. In this study, 19 watershed variables were evaluated in five categories (hydrological, physiographic, point sources, land use, and soil properties) to determine the characteristics that influenced variable nitrate nitrogen (NO3-N) concentrations in 17 Nebraska watersheds with known high NO3-N export rates. Each characteristic was derived from publicly-available datasets in an effort to develop a multiregional method. Of the 19 variables evaluated, 10 variables (developed, cropland, herbaceous, forest, excessively-drained soils, precipitation, base-flow index, slope, organic matter and point sources) were identified to statistically influence stream NO3-N concentrations. The 17 watersheds were divided into five subset groups using principal component analysis. Distributions of the 10 watershed variables were then used to determine the most applicable BMPs for NO3-N reductions for each stream subset: excessively drained with high baseflow index (Groups 1 and 2), dominantely row crop land usage with well-drained soils, higher precipitation, and an increased tendency for surface runoff concerns (Group 3), highly developed watersheds (Group 4), and single river dominated by wastewater treatment plant discharge (Group 5). Based on the most influential variables a variety of BMPs were recommended, including N fertilizer application management and accounting for N credit from mineralization and NO3-N in irrigation water (Groups 1 and 2), installation of riparian buffers and wetlands (Group 3), urban BMPs such as bioretention cells and permeable pavement (Group 4), and upgrades to the wastewater treatment plant (Group 5). This study provides an improved technique for facilitating watershed management by linking BMPs directly to the characteristics of each watershed to reduce current nitrate export.

Diversity in the knowledge, mindset, strategies, and tools that growers use to manage irrigation and fertilizer nitrogen culminates in diversity in profitability and environmental impact among farms. As growers, academia, and industry strive together to tackle the technological and non-technological challenges impeding better irrigation and fertilizer nitrogen management, a science-based evaluation of grower input amount and timing becomes an important initial step in the process. Providing such valuable feedback to growers is a high-priority objective for the University of Nebraska-Lincoln Testing Ag Performance Solutions (UNL-TAPS) farm management competition. In this competition, each team of mostly growers made management decisions for field corn in three replicated plots within the same field at the West Central Research and Extension Center in North Platte, NE, and vied for maximum profitability and most judicious input management. The 2017 dataset affirmed existing theory predicting that many efficiency indices strictly decrease in value with increasing seasonal input amount and thus would fail to point towards an appropriate input level. Furthermore, grain yield was heavily affected by cultivar choice, so efficiency indices that depend on yield actually obscured rather than facilitated the evaluation of irrigation and fertilizer nitrogen management. An alternate evaluation approach is to compare a grower’s seasonal input amount or input temporal distribution against an appropriate range enveloped by university recommendations on the high end and observed yield-limiting thresholds on the low end. Where irrigation and fertilizer nitrogen are relatively inexpensive and where producing near-maximum yield is optimal, this approach is suitable for analyzing an input in isolation and for analyzing multiple inputs simultaneously.

Irrigation is an important adaptation to reduce crop yield loss due to water stress from both soil water deficit (low soil moisture) and atmospheric aridity (high vapor pressure deficit, VPD). Traditionally, irrigation has primarily focused on soil water deficit. Observational evidence demonstrates that stomatal conductance is co-regulated by soil moisture and VPD from water supply and demand aspects. Here we use a validated hydraulically-driven ecosystem model to reproduce the co-regulation pattern. Specifically, we propose a plant-centric irrigation scheme considering water supply-demand dynamics (SDD), and compare it with soil-moisture-based irrigation scheme (management allowable depletion, MAD) for continuous maize cropping systems in Nebraska, United States. We find that, under current climate conditions, the plant-centric SDD irrigation scheme combining soil moisture and VPD, could significantly reduce irrigation water use (−24.0%) while maintaining crop yields, and increase economic profits (+11.2%) and irrigation water productivity (+25.2%) compared with MAD, thus SDD could significantly improve water sustainability., Irrigation is the most important use of water. A newly developed irrigation management scheme leads to a significant reduction in water use and increase in economic gains while maintaining crop yields, presenting opportunities for real-world impacts under current and future climate conditions.

Improvements in soil water balance modeling can be beneficial for optimizing irrigation management to account for spatial variability in soil properties and evapotranspiration (ET). A remote-sensing-based ET and water balance model was tested for irrigation management in an experiment at two University of Nebraska-Lincoln research sites located near Mead and Brule, Nebraska. Both fields included a center pivot equipped with variable rate irrigation (VRI). The study included maize in 2015 and 2016 and soybean in 2016 at Mead, and maize in 2016 at Brule, for a total of 210 plot-years. Four irrigation treatments were applied at Mead, including: VRI based on a remote sensing model (VRI-RS); VRI based on neutron probe soil water content measurement (VRI-NP); uniform irrigation based on neutron probe measurement; and rainfed. Only the VRI-RS and uniform treatments were applied at Brule. Landsat 7 and 8 imagery were used for model input. In 2015, the remote sensing model included reflectance-based crop coefficients for ET estimation in the water balance. In 2016, a hybrid component of the model was activated, which included energy-balance-modeled ET as an input. Both 2015 and 2016 had above-average precipitation at Mead; subsequently, irrigation amounts were relatively low. Seasonal irrigation was greatest for the VRI-RS treatment in all cases because of drift in the water balance model. This was likely caused by excessive soil evaporation estimates. Irrigation application for the VRI-NP at Mead was about 0 mm, 6 mm, and –12 mm less in separate analyses than for the uniform treatment. Irrigation for the VRI-RS was about 40 mm, 50 mm, and –98 mm greater in separate analyses than the uniform at Mead and about 18 mm greater at Brule. For maize at Mead, treatment effects were primarily limited to hydrologic responses (e.g., ET), with differences in yield generally attributed to random error. Rainfed soybean yields were greater than VRI-RS yields, which may have been related to yield loss from lodging, perhaps due to over-irrigation. Regarding the magnitude of spatial variability in the fields, soil available water capacity generally ranked above ET, precipitation, and yield. Future research should include increased cloud-free imagery frequency, incorporation of soil water content measurements into the model, and improved wet soil evaporation and drainage estimates.

Cover crop (CC) species selection can contribute to reducing soil penetration resistance (brassica species), improved soil nitrogen (N) cycling (legume species), and suppression of weeds (grass species). However, one of the main concerns about including CCs in water-limited environments is soil water use and the consequences to subsequent crops. To determine the effects of individual CC species under water-limited environments, we evaluated fall and spring CC biomass produced, and soil water and N content, penetration resistance, weed density and biomass during the maize growing season, and maize grain yield. The experiment was conducted under a winter wheat-maize-fallow rotation at two locations (North Platte and Grant, NE) during 2016−2017 and 2017−2018 (four site-years). Treatments consisted of seven popular CC species plus a control (fallow), planted after winter wheat harvest. Spring oats, Siberian kale, and purple top turnip produced greater fall biomass, while cereal rye produced the greatest amount of spring biomass. However, cereal rye reduced soil volumetric water content in North Platte 2016−2017 and increased soil penetration resistance from 20–30 cm soil depth across site-years likely due to soil water use. Spring cover crop growth suppressed weeds early in the maize growing season. Due to its aboveground biomass production, cereal rye decreased weed density and biomass by 80 and 88 %, respectively, compared to the fallow treatment. On the other hand, except for brassicas, CCs decreased N levels in the soil during maize growing season, and all CC species reduced maize grain yield up to 30 % compared to fallow (except spring oats). Spring oats can be an alternative to cereal rye as CC species for semi-arid regions. However, since CCs did not promote any maize yield gain, our findings suggest that producers should use caution when incorporating CCs in their cropping systems in water-limited environments. This research provides valuable information on the potential impact of CCs on rainfed maize production, as well as help producers and agronomists develop better CC management programs for cropping systems in semi-arid regions.

Despite the potential to increase soil organic matter, cycle soil nutrients, and suppress weeds, there is a concern that cover crops (CCs) soil water use negatively impacts subsequent crops in water-limited environments. Cover crop management practices such as planting and termination timing may mitigate the detrimental impacts of CCs in semi-arid cropping systems. To determine the effects of CCs under water-limited environments, we evaluated the total CC biomass produced in the fall and spring, soil water content during the subsequent maize growing season, weed density and biomass, crop residue, and soil nutrients at the maize V6 development stage, and maize productivity. The experiment was conducted under a wheat-maize-fallow rotation at two sites (Grant and North Platte, NE) during 2016−2017 and 2017−2018. Treatments consisted of three planting timings after winter wheat harvest and three CC termination timings and fallow (no CCs) before maize establishment. Planting CCs shortly after winter wheat harvest increased CC biomass in the fall and early spring compared to late planting. Cover crops terminated early in the spring reduced weed biomass (−70 %), while CCs terminated late in the spring reduced both weed density (−56 %) and biomass (−82 %) compared to fallow. Due to above-normal precipitation during the experiment, soil water measured at 0−20 cm soil depth was impacted by CCs only at Grant 2016−2017. Cover crops terminated late in the spring increased crop residue biomass but decreased total nitrogen at 0−10 cm soil depth by 17 %, and decreased soil nitrate at the 10−20 cm soil depth up to 26 % compared to fallow. Cover crops planted late in the summer (August) and terminated late in the spring (May) had the most detrimental impact on maize grain yield. Late termination of CCs in the spring reduced corn grain yield by up to 20 % compared to fallow. Despite enhanced weed suppression and crop residue, CCs decreased soil nitrogen and maize grain yield, especially when terminated late in the spring. Producers in semi-arid regions of the Great Plains willing to incorporate CCs should use caution when selecting management strategies for their CCs to minimize maize grain yield and economic losses.

Increasing nitrogen use efficiency (NUE) in irrigated corn production is of great importance to overall agricultural sustainability. Studies have shown that crop canopy sensors can aid in this pursuit as they allow for the determination of nitrogen (N) requirements in split applications later in the growing season. Fertigation can also increase NUE as many split applications can be conducted. If crop canopy sensors could be used to direct N fertigation rates, overall NUE may be increased even further. However, in some cases, N differences may need to be determined later in the growing season after corn has tasseled, which can cause issues with crop canopy sensor readings. Therefore, a study was initiated to evaluate the potential of a crop canopy sensor to differentiate between N levels at two corn (Zea mays) growth stages (R1 and R3) after the corn had tasseled. The sensor was placed in three orientations to evaluate which orientation best determined the corn N status across two sensor-calculated...

Reference evapotranspiration is very important parameter in the hydrological, agricultural and environmental studies and is accurately estimated by the FAO Penman-Monteith equation (FAO-PM) under different climatic conditions. However, due to data requirement of the FAO-PM equation, there is a need to investigate the applicability of alternative ETo equations under limited data. The objectives of this study were to evaluate twelve mass transfer based reference evapotranspiration equations and determine the impact of ETo equation on long term water management sustainability in Tanzania and Kenya. The results showed that the Albrecht, Brockamp-Wenner, Dalto, Meyer, Rohwer and Oudin ETo equations systematically overestimated the daily ETo at all weather stations with relative errors that varied from 34% to 94% relative to the FAO-PM ETo estimates. The Penman, Mahringer, Trabert, and the Romanenko equations performed best across Tanzania and the South Western Kenya with root mean squared errors ranging from 0.98 to 1.48 mm/day, which are relatively high and mean bias error (MBE) varying from -0.33 to 0.02 mm/day and the absolute mean error (AME) from 0.79 to 1.16 mm/day. For sustainable water management, the Trabert equation could be adopted at Songea, the Mahringer equation at Tabora, the Dalton and/or the Rohwer equations at Eldoret, the Romanenko equation at Dodoma, Songea and Eldoret. However, regional calibration of the most performing equation could improve water management at regional level.

Variable rate irrigation (VRI) may improve center pivot irrigation management, including deficit irrigation. A remote-sensing-based evapotranspiration model was implemented with Landsat imagery to manage irrigations for a VRI equipped center pivot irrigated field located in West-Central Nebraska planted to maize in 2017 and soybean in 2018. In 2017, the study included VRI using the model and uniform irrigation using neutron attenuation for full irrigation with no intended water stress (VRI-Full and Uniform-Full treatments, respectively). In 2018, two deficit irrigation treatments were added (VRI-Deficit and Uniform-Deficit, respectively) and the model was modified in an attempt to reduce water balance drift; model performance was promising, as it was executed unaided by measurements of soil water content throughout the season. VRI prescriptions did not correlate well with available water capacity (R2 < 0.4); however, they correlated better with modeled ET in 2018 (R2 = 0. 69, VRI-Full; R2 = 0.55, VRI-Deficit). No significant differences were observed in total intended gross irrigation depth in 2017 (VRI-Full = 351 mm, Uniform Full = 344). However, in 2018, VRI resulted in lower mean prescribed gross irrigation than the corresponding uniform treatments (VRI-Full = 265 mm, Uniform Full = 282 mm, VRI-Deficit = 234 mm, and Uniform Deficit = 267 mm). Notwithstanding the differences in prescribed irrigation (in 2018), VRI did not affect dry grain yield, with no statistically significant differences being found between any treatments in either year (F = 0.03, p-value = 0.87 in 2017; F = 0.00, p-value = 0.96 for VRI/Uniform and F = 0.01, p-value = 0.93 for Full/Deficit in 2018). Likewise, any reduction in irrigation application apparently did not result in detectable reductions in deep percolation potential or actual evapotranspiration. Additional research is needed to further vet the model as a deficit irrigation management tool. Suggested model improvements include a continuous function for water stress and an optimization routine in computing the basal crop coefficient.

Irrigation is critical to sustain agricultural productivity in dry or semi-dry environments, and center pivots, due to their versatility and ruggedness, are the most widely used irrigation systems. To effectively use center pivot irrigation systems, producers require tools to support their decision-making on when and how much water to irrigate. However, currently producers make these decisions primarily based on experience and/or limited information of weather. Ineffective use of irrigation systems can lead to overuse of water resources, compromise crop productivity, and directly reduce producers’ economic return as well as bring negative impacts on environmental sustainability. In this paper, we surveyed existing precision irrigation research and tools from peer-reviewed literature, land-grant university extension and industry products, and U.S. patents. We focused on four challenge areas related to precision irrigation decision-support systems: (a) data availability and scalability, (b) quantification of plant water stress, (c) model uncertainties and constraints, and (d) producers’ participation and motivation. We then identified opportunities to address the above four challenge areas: (a) increase the use of high spatial-temporal-resolution satellite fusion products and inexpensive sensor networks to scale up the adoption of precision irrigation decision-support systems; (b) use mechanistic quantification of ‘plant water stress’ as triggers to improve irrigation decision, by explicitly considering the interaction between soil water supply, atmospheric water demand, and plant physiological regulation; (c) constrain the process-based and statistical/machine learning models at each individual field using data-model fusion methods for scalable solutions; and (d) develop easy-to-use tools with flexibility, and increase governments’ financial incentives and support. We conclude this review by laying out our vision for precision irrigation decision-support systems for center pivots that can achieve scalable, economical, reliable, and easy-to-use irrigation management for producers.

The future economic and agricultural sustainability of semiarid western Nebraska will largely depend on more efficient utilization of the declining groundwater resources. The scope of this research was to evaluate the maize hybrid yield, water productivity (WP; i.e. grain yield produced per unit of water consumed), and irrigation water productivity (IWP; i.e. increase in grain yield per unit of irrigation water applied) across a range of semiarid climatic conditions (i.e. drought, normal, and wet) and irrigation treatments. Total of 13 maize hybrids were evaluated under full irrigation (FI), deficit irrigation (DI, receiving ~50% less irrigation water than FI), and dryland (DRY; rainfall only) at the University of Nebraska-Lincoln Brule Water Laboratory near Brule, Nebraska, in 2011 and 2012 and Bayer’s Gothenburg Water Utilization Learning Center near Gothenburg, Nebraska, in 2010 and 2011 (i.e. four site-years). Compared to FI, DI caused yield reduction of as much as 33% in a dry, 11% in a normal, and 2% in a wet year, resulting in consequently 22–47% improvement in IWP. Depending on site-year and irrigation level, a difference of up to 7.2 t ha-1, 3.6 kg m-3, and 5.9 kg m-3 was observed in yield, WP, and IWP, respectively, as a consequence of hybrid selection, with few top-performing hybrids yielding similarly under DI and FI in a normal and/or wet year. This study highlights the impact hybrid selection and DI have on crop water productivity (WP) and IWP as well as provides insight into strategies that can maintain productivity and profitability in water limited environments.

Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what level of priority should stakeholders commit to in order to obtain these data? Before fully addressing these questions a remaining challenge is the complex nature of spatiotemporal yield variation. Here, a methodological framework is presented to separate the spatial and temporal components of crop yield variation at the subfield level. The framework can also be used to quantify the benefits of different data types on the predicted crop yield as well to better understand the connection of that data to underlying mechanisms controlling yield. Here, fine-resolution (10 m) datasets were assembled for eight 64 ha field sites, spanning a range of climatic, topographic, and soil conditions across Nebraska. Using Empirical Orthogonal Function (EOF) analysis, we found the first axis of variation contained 60–85 % of the explained variance from any particular field, thus greatly reducing the dimensionality of the problem. Using Multiple Linear Regression (MLR) and Random Forest (RF) approaches, we quantified that location within the field had the largest relative importance for modeling crop yield patterns. Secondary factors included a combination of vegetation condition, soil water content, and topography. With respect to predicting spatiotemporal crop yield patterns, we found the RF approach (prediction RMSE of 0.2−0.4 Mg/ha for maize) was superior to MLR (0.3−0.8 Mg/ha). While not directly comparable to MLR and RF the EOF approach had relatively low error (0.5–1.7 Mg/ha) and is intriguing as it requires few calibration parameters (2–6 used here) and utilizes the climate-based aridity index, allowing for pragmatic long-term predictions of subfield crop yield.

Interreplicate variability—the spread in output values among units of the same sensor subjected to essentially the same condition—can be a major source of uncertainty in sensor data. To investigate the interreplicate variability among eight electromagnetic soil moisture sensors through a field study, eight units of TDR315, CS616, CS655, HydraProbe2, EC5, 5TE, and Teros12 were installed at a depth of 0.30 m within 3 m of each other, whereas three units of AquaSpy Vector Probe were installed within 3 m of each other. The magnitude of interreplicate variability in volumetric water content (θv) was generally similar between a static period near field capacity and a dynamic period of 85 consecutive days in the growing season. However, a wider range of variability was observed during the dynamic period primarily because interreplicate variability in θv increased sharply whenever infiltrated rainfall reached the sensor depth. Interreplicate variability for most sensors was thus smaller if comparing θv changes over several days that excluded this phenomenon than if comparing θv directly. Among the sensors that also reported temperature and/or apparent electrical conductivity, the sensors exhibiting the largest interreplicate variability in these outputs were characterized by units with consistently above or below average readings. Although manufacturers may continue to improve the technology in and the quality control of soil moisture sensors, users would still benefit from paying greater attention to interreplicate variability and adopting strategies to mitigate the consequences of interreplicate variability.

Variable Rate Irrigation (VRI) considers spatial variability in soil and plant characteristics to optimize irrigation management in agricultural fields. The advent of unmanned aircraft systems (UAS) creates an opportunity to utilize high-resolution (spatial and temporal) imagery into irrigation management due to decreasing costs, ease of operation, and reduction of regulatory constraints. This research aimed to evaluate the use of UAS data for VRI, and to quantify the potential of VRI in terms of relative crop and water response. Irrigation treatments were: (1) VRI using Landsat imagery (VRI-L), (2) VRI using UAS imagery (VRI-U), (3) uniform (U), and (4) rainfed (R). An updated remote-sensing-based evapotranspiration and water balance model, incorporating soil water measurements, was used to make prescriptions for the VRI treatments at a field site in eastern Nebraska. In 2017, the mean prescribed seasonal irrigation depth (Ip) for VRI-L was significantly greater (α = 0.05) than the Ip for U for soybean. In 2018, Ip for soybean was greatest for VRI-U treatment followed by the U and VRI-L treatments, with all being significantly different from each other. No significant differences in Ip for maize were observed in 2017 or 2018. In all crop-year combinations, the VRI and U treatments had significantly greater evapotranspiration (ET) than the R treatment. Yield differences among treatments were not significant (except for rainfed maize compared to VRI-L in 2017). For maize in 2017, IWUE for VRI-L was comparable to the U treatment. The UAS imagery was a better match for the scale of crop management than Landsat imagery, particularly for thermal data. The multispectral UAS data was successfully used in the crop coefficient ET model for real-time irrigation, but using UAS to determine accurate canopy temperatures for surface energy balance modeling remains a challenge.

The unavailability of some meteorological variables, especially solar radiation and wind speed, is the main constraint for reference evapotranspiration (ETo) estimation using the standard U...

With uncertainty in future irrigation water availability and regulations on nutrient application amounts, experimentally determined effects of “controllable” management strategies such as nitrogen (N), water, and their combination on crop water productivity (CWP, also known as crop water use efficiency) and actual evapotranspiration (ET a ) are essential. The effects of various N application rates (0, 84, 140, 196, and 252 kg ha -1 ) under fully irrigated (FIT), limited irrigation (75% FIT), and rainfed conditions on maize (Zea mays L.) yield and various CWP indices were investigated in 2011 and 2012 growing seasons under linear-move sprinkler irrigation in south central Nebraska. CWP was presented as crop water use efficiency (CWUE), irrigation water use efficiency (IWUE), and evapotranspiration water use efficiency (ETWUE). The seasonal rainfall amounts in 2011 and 2012 were 371 mm and 296 mm, respectively, as compared with the long-term average of 469 mm. Two experimental seasons were contrasted with extreme warmer temperatures, greater solar radiation, and lower rainfall in 2012. Maximum grain yield of 12.68 metric tons ha -1 and 14.42 tons ha -1 was observed in 2011 and 2012, respectively, under the fully irrigated and 252 kg N ha -1 treatment. Grain yield was linearly related to ET a and curvilinearly related to N and irrigation application amounts. Lower N treatments were more susceptible to interannual effects on the grain yield response to irrigation water amount. CWUE ranged from 1.52 kg m -3 (FIT and 84 kg N ha -1 ) to 2.58 kg m -3 (rainfed and 196 kg N ha -1 ) with an average of 2.15 kg m -3 in 2011, and from 1.49 kg m -3 (FIT and 0 kg N ha -1 ) to 2.72 kg m -3 (rainfed and 252 kg N ha -1 ) with an average of 2.33 kg m -3 in 2012. CWUE had a positive quadratic relationship with N application amount and decreased with both the presence and amount of irrigation at a given N application amount. The maximum IWUE for 75% FIT and FIT in 2011 was 1.80 kg m -3 (252 kg N ha -1 ) and 1.51 kg m -3 (252 kg N ha -1 ), respectively, whereas in 2012 the maximum IWUE values were 1.40 kg m -3 (196 kg N ha -1 ) and 1.78 kg m -3 (252 kg N ha -1 ), respectively. A curvilinear relationship was observed between IWUE and N application amount. An optimal N application amount of 196 kg ha -1 was identified for the pooled data to maximize the increase in grain yield above rainfed conditions per unit of applied irrigation water under limited irrigation management practices. In 2011, ETWUE ranged from 0.22 kg m -3 (140 kg N ha -1 ) to 1.46 kg m -3 (196 kg N ha -1 ) and from -0.21 kg m -3 (84 kg N ha -1 ) to 3.74 kg m -3 (252 kg N ha -1 ) for 75% FIT and FIT, respectively, whereas in 2012 ETWUE ranged from -0.07 kg m -3 (0 kg N ha -1 ) to 1.87 kg m -3 (252 kg N ha -1 ) and from -0.14 kg m -3 (0 kg N ha -1 ) to 3.65 kg m -3 (196 kg N ha -1 ) for 75% FIT and FIT, respectively. The results support that there is an optimal N level for each irrigation regime and, in general, lower N application amounts are required to reach maximum productivity (e.g., CWUE) under limited and rainfed conditions as compared with the FIT. In other words, there is an optimal N application amount to maximize the effectiveness of irrigation water on increasing grain yield above rainfed yields. The optimal N level for maximum productivity varied not only between the irrigation levels, but also exhibited interannual variability for the same irrigation level, indicating that these variables are impacted by the climatic conditions.

Understanding the relationships between climatic variables and soil physical and chemical properties with crop yields on large scales is critical for evaluating crop productivity to make better assessments of local and regional food security, policy, land and water resource allocation, and management decisions. In this study, ordinary least squares (OLS) regression models were developed to predict irrigated and rainfed maize and soybean yields at the county level as a function of explanatory variables [precipitation (P), actual crop evapotranspiration (ETa), organic matter content (OMC), cation exchange capacity (CEC), clay content (CC), and available soil water capacity (ASW)] of the dominant soil type in each of the 93 counties in Nebraska. Models were developed for the statewide average dataset (state models) as well as for the four major climatic zones (zonal models). Spline interpolation was used to spatially interpolate all independent variables across all 93 counties. The results of the OLS state models showed a very good performance for predicting rainfed maize and soybean yields. For rainfed maize, about 73% of the variation in yield (RMSD = 867 kg ha-1) was explained by ETa alone, and 83% of yield variability (RMSD = 690 kg ha-1) was explained by the model Yield = f(ETa, P, ASW, CEC, CC). For rainfed soybean, about 69% of the variability (RMSD = 238 kg ha-1) was explained by ETa alone, and a maximum of 85% (RMSD = 164 kg ha-1) of the variability was explained by the model Yield = f(ETa, P, ASW, CEC, CC). No additional variation in yield was explained by adding OMC to the rainfed maize and soybean yield models. Less correlation was found between the predicted and observed yields for irrigated maize and soybean than for the rainfed yields for both crops. For irrigated maize and soybean, a maximum of 45% (RMSD = 533 kg ha-1) and 36% (RMSD = 218 kg ha-1) of the variability in yield was explained by the models Yield = f(ETa, P, ASW) and Yield = f(ETa, P, ASW, CEC, CC, OMC), respectively. For the rainfed crops, ETa played a major role in predicting yield, whereas P and ASW played a major role in predicting irrigated yields. ETa and P accounted for 96%, 73%, and 67% of the total explained variation in rainfed soybean yield for zones 2 (drier), 3, and 4 (wetter), respectively, whereas soil physical and chemical properties accounted for 4%, 27%, and 33%, respectively. Unlike rainfed conditions, irrigated maize and soybean yield predictions were improved by applying the zonal models rather than the state models.

Field research was conducted at the University of Nebraska-Lincoln South Central Agricultural Laboratory (SCAL) located near Clay Center, NE, in the growing seasons of 2011 to 2014. A partial economic analysis was conducted for maize (Zea mays L.) at nitrogen (N) fertilizer treatments of 0, 84, 140, 196, and 252 kg ha−1 under full irrigation (FIT), limited irrigation (75% FIT), and rainfed settings for all growing seasons and then compared to crop water productivity (CWP) measured as crop water use efficiency (CWUE) and irrigation water use efficiency (IWUE). Nitrogen fertilizer increased CWUE and IWUE in all growing seasons. The CWUE values ranged from 0.90 to 2.81 kg m−3 and the IWUE values ranged from −1.01 to 3.24 kg m−3. Operational costs and net income varied among treatments and across years. Irrigation and N fertilizer rate had an interacting effect (P0.05

One of the common methods for estimating actual evapotranspiration (ETa) is the two-step approach, which relates crop-specific crop coefficients (Kc) to a reference surface ET, typically al...